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While text classification has been identified for some time as a promising application area for Artificial Intelligence, so far few deployed applications have been described. In this paper we present a spam filtering system that uses example-based machine learning techniques to train a classifier from examples of spam and legitimate email. This approach has the advantage that it can personalise to the specifics of the user’s filtering preferences. This classifier can also automatically adjust over time to account for the changing nature of spam (and indeed changes in the profile of legitimate email). A significant software engineering challenge in developing this system was to ensure that it could interoperate with existing email systems to allow easy management of the training data over time. This system has been deployed and evaluated over an extended period and the results of this evaluation are presented here.
Delany, S.J. & Cunningham, P. & Smith, B. (2006). ECUE: a spam filter that uses machine learning to track concept drift. Proceedings of the 2006 conference on ECAI : 17th European Conference on Artificial Intelligence August 29 -- September 1, Riva del Garda, Italy2006. doi:10.21427/740t-je03